Method

Younger3DTracker [Y3D]
https://github.com/yangyingchun1999/Young3DMOT

Submitted on 8 Mar. 2023 14:01 by
yang yingchun (chongqing university)

Running time:0.03 s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
an improved Kalman Filter based Lidar
Parameters:
kalman
state_func_covariance=100
measure_func_covariance=0.001
prediction_score_decay=0.02
LiDAR_scanning_frequency=10
Latex Bibtex:
Improved Kalman filter-based 3D multi-target tracking,\inproceedings{IEEE sensors journal}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 77.32 % 75.08 % 80.32 % 79.77 % 85.55 % 83.16 % 91.01 % 87.90 %

Benchmark TP FP FN
CAR 30972 3420 1096

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.33 % 86.61 % 86.87 % 184 74.27 %

Benchmark MT rate PT rate ML rate FRAG
CAR 76.31 % 20.77 % 2.92 % 652

Benchmark # Dets # Tracks
CAR 32068 990

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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